{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>88.699997</td>\n",
       "      <td>88.940002</td>\n",
       "      <td>86.040001</td>\n",
       "      <td>86.099998</td>\n",
       "      <td>24029600</td>\n",
       "      <td>86.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-02</th>\n",
       "      <td>86.269997</td>\n",
       "      <td>88.199997</td>\n",
       "      <td>85.611000</td>\n",
       "      <td>87.059998</td>\n",
       "      <td>21469700</td>\n",
       "      <td>87.059998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-03</th>\n",
       "      <td>88.099998</td>\n",
       "      <td>89.940002</td>\n",
       "      <td>87.650002</td>\n",
       "      <td>88.099998</td>\n",
       "      <td>18485700</td>\n",
       "      <td>88.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-06</th>\n",
       "      <td>89.150002</td>\n",
       "      <td>89.650002</td>\n",
       "      <td>88.059998</td>\n",
       "      <td>88.309998</td>\n",
       "      <td>9268400</td>\n",
       "      <td>88.309998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-07</th>\n",
       "      <td>87.949997</td>\n",
       "      <td>89.699997</td>\n",
       "      <td>87.059998</td>\n",
       "      <td>87.669998</td>\n",
       "      <td>12791700</td>\n",
       "      <td>87.669998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close    Volume  Adj Close\n",
       "Date                                                                       \n",
       "2014-10-01  88.699997  88.940002  86.040001  86.099998  24029600  86.099998\n",
       "2014-10-02  86.269997  88.199997  85.611000  87.059998  21469700  87.059998\n",
       "2014-10-03  88.099998  89.940002  87.650002  88.099998  18485700  88.099998\n",
       "2014-10-06  89.150002  89.650002  88.059998  88.309998   9268400  88.309998\n",
       "2014-10-07  87.949997  89.699997  87.059998  87.669998  12791700  87.669998"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_datareader\n",
    "df = pandas_datareader.DataReader('BABA', data_source='yahoo', start='2014-10-01')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?df.plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18c39518>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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wh90WhwezUZFlNVE8xIxWUaY1nK259+IZGGLUEd2yvBKPP8CNj7zHP2PMfCSj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\nIk4k0RJCCCGEiJP/D3tQXGqlHZWdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19210da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].plot(kind='line', figsize=[10,5], legend=True, title='BABA', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#df['mvg30']\n",
    "df['mvg30']  = df['Close'].rolling(window=30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1934dc50>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4SD3REGsxERdp4uHFuym1OXl3Yy52l5crT6zTaHv143D4ezjvMb3w95jzwNCC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1935bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Close', 'mvg30']].plot(kind = 'line', figsize=[10,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x196e8e10>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9HMB44EzgsYj4kvOdn2J+mWt3fvPzuxmq8Z4GxqIe7WW5sl+A852fZH6Za3d+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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196d2438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ix[df.index >= '2017-04-01'   ,  'Volume'].plot(kind='bar', legend=True, title='BABA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['diff'] =  df['Close'] - df['Open']\n",
    "df['rise'] = df['diff'] > 0 \n",
    "df['fall'] = df['diff'] < 0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aa36a58>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aQ8XmSTGfXVnMZy8ADgaKnuPUba/UzPm+M8SQA34IjNahZ2Op2Dwr5rMPAWOB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\nUbGJSOL8HyZ2BWOUUq+yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa4bcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['rise', 'fall']].sum().plot(kind='pie', figsize=[5,5], counterclock=True, startangle = 90, legend=True, title='rise v.s. fall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc612e8>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mxlswGZ8vSdpxWztJnb7lVw6zEEnSaNlaQNQEy5KkndzWppiOTPIovSOJPZtlmvWqqp8f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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8f2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].plot(kind='hist', title='histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['ret'] = df['Close'].pct_change(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bdedb00>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZBEGSVyd5KMmXkuyaRQ2SpJGpnxpKchbwPuAq4BHgziT7qur+IfY3y6cmDX3KZVJPkJJa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1be11a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ret'].plot(kind = 'hist', bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf462b0>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf674a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BABA</th>\n",
       "      <th>BIDU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>86.099998</td>\n",
       "      <td>212.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-02</th>\n",
       "      <td>87.059998</td>\n",
       "      <td>214.800003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-03</th>\n",
       "      <td>88.099998</td>\n",
       "      <td>216.449997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-06</th>\n",
       "      <td>88.309998</td>\n",
       "      <td>217.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-07</th>\n",
       "      <td>87.669998</td>\n",
       "      <td>215.630005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BABA        BIDU\n",
       "Date                             \n",
       "2014-10-01  86.099998  212.029999\n",
       "2014-10-02  87.059998  214.800003\n",
       "2014-10-03  88.099998  216.449997\n",
       "2014-10-06  88.309998  217.199997\n",
       "2014-10-07  87.669998  215.630005"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas_datareader\n",
    "df = pandas_datareader.DataReader(['BABA', 'BIDU'], data_source='yahoo', start='2014-10-01')['Close']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c19ef28>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VmvN//vcF6DtJlhNu8vZFt4AxV4uHV8Y6/2J4kJmkXYZXPu4WTuqwPiNaxQWw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\n0Un+HzgWs3ckR0tTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1c6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='line', figsize=[10,5], title=\"BABA v.s. BIDU\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rets = df.pct_change(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BABA</th>\n",
       "      <th>BIDU</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-02</th>\n",
       "      <td>0.011150</td>\n",
       "      <td>0.013064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-03</th>\n",
       "      <td>0.011946</td>\n",
       "      <td>0.007682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-06</th>\n",
       "      <td>0.002384</td>\n",
       "      <td>0.003465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-07</th>\n",
       "      <td>-0.007247</td>\n",
       "      <td>-0.007228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BABA      BIDU\n",
       "Date                          \n",
       "2014-10-01       NaN       NaN\n",
       "2014-10-02  0.011150  0.013064\n",
       "2014-10-03  0.011946  0.007682\n",
       "2014-10-06  0.002384  0.003465\n",
       "2014-10-07 -0.007247 -0.007228"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c6a5128>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8NTIr2hfVFPUP4rn9I3u6E0VvAGqlUN+cNJZizWLnFNj2rSALIuEQ9Kz+ctru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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c69c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets.plot(kind='scatter', x='BABA', y='BIDU' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BABA</th>\n",
       "      <th>BIDU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BABA</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.506644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIDU</th>\n",
       "      <td>0.506644</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          BABA      BIDU\n",
       "BABA  1.000000  0.506644\n",
       "BIDU  0.506644  1.000000"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = rets.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x1c55edd8>,\n",
       "  <matplotlib.axis.YTick at 0x1c54aac8>],\n",
       " <a list of 2 Text yticklabel objects>)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6pqouqqoLgQ8Dnx45vwG4F3j7yPE7u/su6M5fu3AiyUnAxcDKJK9evqo3banfbcGdVfW6\nqjoX+ABwQ5I3H7Za6pAZ8JbPicAvF3aSvAZ4KfBRBoHvBTJYmnDC8H0MguPXgVuB9ctVWf3W7/xu\ni6mqHcAngeuWvUbqzRGztOwIcVySHcCLgdOANw2dW88gaN0JnJvk1Kp6vDt3WXffy4GngY8M3beB\nwT+sx4EvAZ9a3q/QpHG/2zj3AR9atlqpd2Z4/VpoGp0HrANuyfMLSjcAt1bVfgaB651D9y00ac8C\nvgB8FiDJqcA5wPeq6sfAb5Kcf7i+TEPG/W7jDF9zoPldzvmaMwa8ZVJVdzFYRH1Kkj9gELi+meSn\nDLK9AzZrgc3AH3ef3wWcDDzS3bd6zH3qwfDvNsHlrwN+1H1+ksFvBUCSl3F0vmDgiGbAWyZJzgNW\nMPiHsAH4eFWt7rbTgdOTHOjtFG8EftJ93gCsW7iPweCF/XjLaOR3G3fdBcDHgBu7Q7cD705ybLf/\nPuA7y1NLHSz78Pq10BcEg+bOe6tqX5L1wFtHrv0Kg+B1D8/34QV4Cnh/ktUMXtfz2+koVfVINzXi\n0qq6Z3m/SlMW+91Gr7ssyXbgeOAJ4ANV9W2AqvpGkouBbUn2Mfif1jWHp/qalEvLJDXDJq2kZhjw\nJDXDgCepGQY8Sc0w4ElqhgFPUjMMeJKa8f/PVf5WwXzVwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c4c2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(corr, cmap='hot', interpolation='none')\n",
    "plt.colorbar()\n",
    "plt.xticks(range(len(corr)), corr.columns)\n",
    "plt.yticks(range(len(corr)), corr.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
